Yazar "Al-Dahawi, Ali" seçeneğine göre listele

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    Development of gravitational search algorithm model for predicting packing density of cementitious pastes
    (ELSEVIER, 2020) Banyhussan, Qais S.; Hanoon, Ammar N.; Al-Dahawi, Ali; Yildirim, Gurkan; Abdulhameed, Ali A.
    Wet packing approach is recently used to design different kinds of concrete. To achieve an optimal particle packing density, the particles should be chosen in a way to fill the voids between larger particles with smaller ones for obtaining a dense and stiff particle structure. The majority of past research on packing density has focused on the evaluation of the particle size distribution of granular matrix to gain improvements in the packing density of cementitious materials, while limited attention has been paid to the construction of a model to estimate the packing density value. To serve that purpose, in this study, a series of 216 collected samples were used for proposing a model. The dataset was divided into two main sets for construction and verification of the model. As the basis for the packing density modeling, use of the gravitational search algorithm (GSA) was proposed. Design of experiment (DOE) software was used to evaluate the contribution of each variable on the proposed model. The outcomes indicate that among different parameters, water amount has the largest effect on the packing density value of cementitious pastes. Moreover, increases in the amounts of supplementary cementitious materials up to certain levels increase the packing density. The coefficient of variation (CoV) of the proposed model is 6.3% which reflects the accuracy and consistency of the model.
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    Self-sensing capability of Engineered Cementitious Composites: Effects of aging and loading conditions
    (ELSEVIER SCI LTD, 2020) Yildirim, Gurkan; Ozturk, Oguzhan; Al-Dahawi, Ali; Ulu, Adem Afsin; Sahmaran, Mustafa
    Self-sensing capability of 7-, 28-, 90- and 180-day-old Engineered Cementitious Composites (ECC) incorporated either with carbon fibers (CF/ECC-CF) at micro-scale or multi-walled carbon nanotubes (CNT/ECC-CNT) and carbon black (CB/ECC-CB) at nano-scale were investigated herein. Mechanical properties (compressive strength, splitting tensile strength/deformation, flexural strength/deformation) of different-age mixtures were evaluated. Control mixture (ECC-Control) without any carbon-based material was also produced and tested for comparison. Depending on the loading condition, equipment utilizing either direct current (DC) or alternating current (AC) was used for self-sensing assessments. Results showed that carbon-based materials generally improve the mechanical properties of ECC-Control specimens depending on the type of carbon-based materials, specimens' age and loading conditions. All specimens sensed different types of damage except 180-day-old ECC-Control specimens loaded under uniaxial compression and splitting tension due to abrupt increments in impedance results exceeding the limits of testing device which revealed the importance of presence of electrically-conductive materials for achieving enhanced self-sensing capability independent of aging, testing configuration/equipment, loading conditions and microcrack characteristics. CF is the best to improve self-sensing capability of ECC-Control specimens for all ages and loading conditions. Self-sensing performances of ECC-CNT and ECC-CB are comparable and utilization of nano-size carbon-based materials is suggested in cases where reversibility in self-sensing is needed. (C) 2019 Elsevier Ltd. All rights reserved.